BLM helicase suppresses recombination at G-quadruplex motifs in transcribed genes

BLM helicase suppresses recombination at G-quadruplex motifs in transcribed genes

van Wietmarschen, Niek; Merzouk, Sarra; Halsema, Nancy; Spierings, Diana C J; Guryev,

Victor; Lansdorp, Peter M

Published in:

Nature Communications

DOI:

10.1038/s41467-017-02760-1

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van Wietmarschen, N., Merzouk, S., Halsema, N., Spierings, D. C. J., Guryev, V., & Lansdorp, P. M.

(2018). BLM helicase suppresses recombination at G-quadruplex motifs in transcribed genes. Nature

Communications, 9(1), [271]. https://doi.org/10.1038/s41467-017-02760-1

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BLM helicase suppresses recombination at

G-quadruplex motifs in transcribed genes

Niek van Wietmarschen

1

, Sarra Merzouk

1

, Nancy Halsema

1

, Diana C.J. Spierings

1

, Victor Guryev

1

& Peter M. Lansdorp

1,2,3

Bloom syndrome is a cancer predisposition disorder caused by mutations in the

BLM helicase

gene. Cells from persons with Bloom syndrome exhibit striking genomic instability

characterized by excessive sister chromatid exchange events (SCEs). We applied single-cell

DNA template strand sequencing (Strand-seq) to map the genomic locations of SCEs. Our

results show that in the absence of BLM, SCEs in human and murine cells do not

occur randomly throughout the genome but are strikingly enriched at coding regions,

speci

fically at sites of guanine quadruplex (G4) motifs in transcribed genes. We propose that

BLM protects against genome instability by suppressing recombination at sites of

G4 structures, particularly in transcribed regions of the genome.

DOI: 10.1038/s41467-017-02760-1

OPEN

1_{European Research Institute for the Biology of Ageing, University of Groningen, University Medical Center Groningen, Antonius Deusinglaan 1, 9713 AV}

Groningen, The Netherlands.2_{Terry Fox Laboratory, British Columbia Cancer Agency, Vancouver, BC V5Z 1L3, Canada.}3_{Department of Medical Genetics,}

University of British Columbia, Vancouver, BC V6T 1Z4, Canada. Correspondence and requests for materials should be addressed to P.M.L. (email:plansdor@bccrc.ca)

123456789

B

loom syndrome (BS) is a rare genetic disorder caused by

mutations in the BLM gene, which encodes the BLM

helicase

1

. Symptoms of the disease include short stature,

immunodeficiency, UV sensitivity, reduced fertility, and a strong

predisposition toward a wide range of cancers. Cells from

BS patients display marked genome instability, characterized by a

10-fold increase in the rate of sister chromatid exchange events

(SCEs) in cells from patients compared with healthy controls

2,3

.

SCEs are a byproduct of double-strand breaks (DSBs) or

collapsed replication forks that are repaired via homologous

recombination (HR)

4,5

. Although SCEs are typically

non-muta-genic, they are considered markers for genome fragility and

somatic mutation rates

6

. BLM antagonizes SCE formation by

dissolving double Holliday junction structures during HR, along

with its partners TOPO3α, RMI1, and RMI2

7,8

_{. BLM also}

promotes regression of stalled replication forks, facilitating fork

restart and preventing fork collapse and the formation of

DSBs

9,10

. BS cells display higher numbers of

γH2Ax foci

11

,

indicating frequent activation of the DNA damage response in the

absence of BLM. It has also been reported that BS cells display

elevated levels of loss of heterozygosity (LOH), due to exchanges

between homologous chromosomes

12–14

. Besides its ability to

regress replication forks and dissolve Holliday junctions, BLM has

been shown to bind and unwind guanine-quadruplex

(G-quad-ruplex, or G4) structures in vitro

15–17

. G4 structures are stable

secondary DNA structures that form at guanine-rich DNA

motifs

18,19

and are known barriers for replication fork

progression

20

.

Although SCEs can be used as a surrogate marker for collapsed

forks and DSBs, their locations could until recently only be

mapped cytogenetically at megabase resolution

21

. This approach

does not allow investigations of the location and potential causes

of fork stalling and recombination in BS. We recently described a

single-cell sequencing-based technique, Strand-seq, which can be

used to map SCEs at kilobase resolution, enabling novel studies of

their locations and potential causes

22,23

. Strand-seq is a single-cell

sequencing technique that relies on selective retention and

sequencing of DNA template strands after DNA replication and

cell division has occurred (Supplementary Fig.

1

a). SCEs are

detected as changes in orientation of DNA template strands

inherited by daughter cells. By sequencing DNA template strands

in single cells, changes in their directionality are identified and

mapped to the genome at kilobase resolution (Supplementary

Fig.

1

a, b).

Here we show that SCEs in BLM-deficient cells occur

fre-quently at sites of G4 motifs, especially those present in

tran-scribed genes. Furthermore, we show that although LOH events

appear to be more frequent in BLM-deficient cells, these events

were exceedingly rare in our study. We propose that besides

LOH, recombination at G4 motifs in transcribed genes is a major

contributor to genome instability and cancer predisposition in BS.

Results

Mapping of SCEs using Strand-seq. To address the question of

whether SCEs occur at random or at specific locations in the

genome, we performed Strand-seq on a panel of eight different

cell lines, four obtained from healthy donors (two primary

fibroblast and two EBV transformed B-lymphocyte cell lines) and

four cell lines from BS patients (two

fibroblast and two B-cell

lines) (see Supplementary Table

1

). We confirmed that the BS cell

lines displayed ~ 10-fold elevated SCE rates compared with wild

type (WT) (Fig.

1

a–d). Current Strand-seq libraries cover on

average ~ 1–2% of the genome due to loss of DNA during

pre-paration of single-cell sequencing libraries and uneven coverage

further limits the resolution of SCE mapping. The median

resolution of individual SCE mapping was ~ 10 Kbp (Fig.

1

e and

Supplementary Fig.

1

b) and

> 95% of all SCE could be mapped to

regions smaller than 100 Kb (Supplementary Table

1

). These

resolutions are several orders of magnitude higher than the

megabase resolutions than can be achieved by conventional SCE

mapping using cytogenetics

21

.

We detected strong correlations between chromosome size and

the number of SCEs on each chromosome (Fig.

1

f, g), as one

would expect if SCEs were randomly distributed on a global level.

However, we also detected higher than expected numbers of

overlapping SCE regions in multiple common fragile sites (CFSs),

e.g., FRA3B (Fig.

1

h) and FRA7B (Supplementary Fig.

1

c), in the

EBV-transformed cell lines. The absence of SCE hotspots in CFSs

in primary

fibroblasts (Table

1

) suggest that this phenotype is

intrinsic to EBV-transformed B-lymphocytes, perhaps as a result

of replication stress induced by viral transformation

24

. This is

consistent with previous observations that SCEs frequently occur

in CFSs in cells undergoing replication stress, presumably due to

replication

fork

stalling

and

collapse

25

.

Strikingly,

SCE

frequencies within CFS hotspots are remarkably similar for the

WT and BS cell lines (SCE were mapped to any given hotspot in

~ 2–9% of libraries), even though BS cells display 10-fold

higher global SCE rates (Table

1

). This suggests that BLM has a

minor role in the processing of stalled or collapsed replication

forks at CFSs.

BS SCEs are enriched in transcribed genes. We next investigated

the distribution of SCEs relative to specific genomic features of

interest (FOIs). We developed a custom algorithm that compares

SCE distributions with simulated random distributions in relation

to a given FOI (see Methods section). For each cell line, we

performed a permutation analysis to calculate the frequency of

actual SCE regions overlapping with an FOI and compared it

against the expected background frequency. This analysis yields

relative SCE enrichments for a given FOI and allows for statistical

assessment of the strength of the association.

We

first turned to transcribed genes, as transcriptional activity

is a known cause of genome instability and mutations through

transcription–replication collisions and the formation of

co-transcriptional R-loops

26,27

. BLM unwinds R-loops and the

absence of BLM has been linked to genome instability at sites

of R-loops

28,29

. To study a possible link between SCE locations

and transcriptional status we assessed the transcriptional activity

in each of our 8 cell lines using RNA-seq. Genes were divided into

two categories based on the number of fragments per kilobase of

processed transcript per million fragments mapped (FPKM)

values: transcribed (FPKM

> 1) and non-transcribed (FPKM < 1),

resulting in an average of 60% (~ 23,000) of all genes classified as

transcribed and 40% (~ 16,000) as non-transcribed. A significant

enrichment of SCE regions overlapping with gene bodies was

found in all BS cell lines, but in none of the WT cell lines

(Fig.

2

a). However, these enrichments were not affected by gene

activity (Supplementary Fig.

S2a, b

). The same results were seen

after subsampling SCE regions from each cell line with the lowest

number of SCEs (WT1), indicating the detected SCE enrichments

are not an analysis artifact caused by the higher numbers of BS

SCEs (Supplementary Fig.

2

c). SCEs were also significantly

enriched in the gene promoter region of BS cells, independent of

the transcriptional status of the associated genes (Supplementary

Fig.

2

d–f). We also investigated if gene expression levels affected

SCE occurrence within those genes. To do this, we divided all

expressed genes into four categories based on their RPKM-values,

ranging from low to high expression, and assessed the number of

SCEs overlapping the genes in each category. We found only

weak-to-moderate correlations between gene expression levels

and SCE occurrence in all eight cell lines (R

2

-values ranging from

0.05 to 0.64), with no differences between the WT and BS cell

lines (Supplementary Fig.

2

g). These results indicate that

transcription by itself does not appear to have a strong role in

SCE formation.

BS SCEs are enriched at G4 motifs. We next considered the

possibility that the intragenic SCE enrichments might be caused

by the presence of G4 in and around genes. BLM is known to

bind and unwind G4 structures in vitro

15–17

and G4 motifs occur

frequently within gene bodies and promoters

30,31

. To assess SCE

chr1 chr2 chr3 chr4 chr5 chr6 chr7 chr8 chr9 _{chr10 chr11 chr12 chr13 chr14 chr15 chr16 chr17 chr18 chr19 chr20 chr21 chr22} chrX chrY BS fibroblast: 39 SCEs WT fibroblast: 4 SCEs

a

b

c

d

f

100 90 80 70 60 50 40 30 20 10 0 SCEs mapped (%) 80 60 40 20 0 75 60 45 30 15 0 WT1 WT2 BS1 BS2 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 5.0 4.0 3.0 2.0 1.0 0.0 Cell line 50 100 150 200 250 50 100 150 200 250 Chromosome size (Mbp)

h

chr3 (p14.2) 500 kb 60,100,000 60,200,000 60,300,000 60,400,000 60,500,000 60,600,000 WT1 WT2 WT3 WT4 BS1 BS2 BS3 BS4 FHIT 60,700,000 60,800,000 60,900,000 hg19 24 29 Chromosome size (Mbp) WT3 WT4 BS4 <10 bp _{<100 bp} <1 Kbp _{<10 Kbp} <100 Kbp <1 Mbp <10 Mbp BS3 Cell line Resolution p = 7.5e–189 p = 1.7e–243 WT1 WT2 WT3 WT4 WT1 WT2 WT3 WT4 BS1 BS2 BS3 BS4 BS1 BS2 BS3 BS4 SCEs/library SCEs/library SCEs/chr/lib SCEs/chr/lib R2 _{= 0.92} R2 _{= 0.92} R2 = 0.90 R2 = 0.90 R2 _{= 0.94} R2 _{= 0.94} R2 = 0.95 R2 = 0.96

e

g

enrichments at G4 motifs, we determined the distributions of the

canonical G4 motif (G

3+

N

1–7

G

3+

N

1−7

G

3+

N

1−7

G

3+

) across the

genome using a custom algorithm, and performed our SCE

enrichment analysis on these regions. For this analysis, we used a

stringent 10 Kb size cutoff for SCE regions included in this

ana-lysis because G4 motifs occur frequently throughout the genome

(~ 8.6 Kb on average) and including larger SCE regions would

result in increased noise because of the high likelihood of

(per-mutated) SCE regions overlapping G4 motifs purely due to their

size. Strikingly, we found significant, ~ 20% enrichments over

expected levels for SCE regions overlapping G4 motifs in the BS

cells, but no enrichments in the WT cells (Fig.

2

b), indicating that

G4 structures are a causal factor for SCE formation in absence of

BLM. We subsequently tested if presence of G4 motifs in genes

had an effect on SCE enrichments by splitting all genes into those

with and those without G4 motifs. Although we detected

sig-nificant SCE enrichments in BS cells for both genes with and

without G4 motifs, these enrichments were stronger for genes

containing at least one G4 motif (Supplementary Fig.

3

a, b),

indicating that the presence of G4 motifs is at least partially

responsible for the SCE enrichments detected in genes in BS cells.

A similar result was seen for SCE overlapping promoters with or

without G4 motifs (Supplementary Fig.

3

c, d). Based on these

results, we decided to further investigate the link between G4

motifs, transcription, and SCE formation in BS cells.

We detected

> 350,000 canonical G4 motifs in the human

genome, consistent with previously reported numbers

32

. However,

cells may harbor only ~ 10,000 actual G4 structures

33

. As our

analysis is based on SCEs overlapping with G4 motifs, we likely

overestimate overlaps with G4 structures in our permutation

analysis, leading to reduced enrichment estimates. The high

prevalence of G4 motifs also means that larger SCE regions are

likely to overlap with at least one G4 motif purely by chance in our

permutation analysis, leading to elevated noise in our analysis and

reducing relative enrichment values of the observed SCE regions.

Using less stringent size cutoffs for SCE regions size than the 10 Kb

cutoff used for Fig.

2

b did indeed decrease the relative SCE

enrichment values for the BS cells, but not the WT cells

(Supplementary Fig.

4

a), although BS SCE enrichments remained

significant for all cutoffs used. Next, we added increasingly large

flanking regions to the observed SCE regions to increase random

overlaps, potentially decreasing SCE enrichment values. We did

indeed observe an inverse relationship between SCE enrichments

and the size of

flanking regions in BS, but not WT cells lines

(Supplementary Fig.

4

b). This result suggests that even at our

stringent 10Kbp cutoff for SCE region size, there is noise present in

the permutation analysis. Taken together, we conclude that actual

SCE enrichments at G4 structures are almost certainly much higher

than reported in this study. As including larger SCE regions only

affects SCE enrichments for BS cell lines, we also conclude that the

enrichments we detect are indeed specific for BS cells.

Besides the canonical G4 motif, we also tested alternative G4

motifs for SCE enrichments. We did detect BS SCE enrichments at

sites containing G4 motifs containing smaller (n1–3) and larger

(n1–12) spacer regions (Supplementary Fig.

4

b, c), although the

enrichments were not as strong as for the canonical motif. This

suggests that the canonical G4 motif is more likely to form

G4 structures and induce SCE formation in vivo, or that BLM

displays some specificity for G4 structures with medium-sized

loops. Significant SCE enrichments were also detected at previously

described

“observed quadruplex regions” (Supplementary Fig.

4

d),

reported to constitute all regions in the genome capable of forming

Fig. 1 High-resolution mapping of SCEs and common fragile site hotspots. a, b Representative Strand-seq libraries generated from a a WTfibroblast and b a BSfibroblast. Mapped DNA template strand reads are plotted on directional chromosome ideograms; reads mapping to the Crick (positive) strand of the reference genome are shown in green, those mapping to the Watson (negative) strand are shown in orange. SCEs are identi_{fied as a switch in template} strand state, indicated by arrowheads.c, d Number of SCEs detected during a single cell cycle in c primaryfibroblasts and d EBV-transformed B-lymphocytes obtained from healthy donor and BS patients. Each grey point represents number of SCEs detected in a single-cell Strand-seq library, red lines indicate mean_{± SD. p-values were calculated using ANOVA. e SCE mapping resolutions across all eight cell lines. Lines represent percentage of the total} number of SCEs mapped at resolutions below indicated values.f, g Correlations between average numbers of SCEs/chromosome/library and chromosome size forf WT and g BS cells.R2-values are color-matched to the cell lines.h Example of SCE hotspot detected within FRA3B (FHIT). Mapped SCE regions for each cell line were uploaded onto the UCSC Genome Browser. Black bars represent genomic locations of SCE regions; size indicates mapping resolution using the BAIT program. Red box indicates the location of the SCE hotspot as detected by Strand-seq

Table 1 SCE hotspots in common fragile sites, related to Fig.

1

Cell line CFS Coordinates SCE hotspot Genes(s) Libraries with SCE in hotspot

SCEs (n) p-value WT3 FRA3B chr3:60,235,449–60,601,404 FHIT 15/334 (4.5%) 2128 3.2e−18 FRA7B chr7:5,880,409–6,120,310 CCZ1, PMS2 11/334 (3.3%) 2128 4.8e−13 FRA7B chr7:6,707,575_{–6,967,206 PMS2CL, CCZ1B} 29/334 (8.7%) 2128 8.9e₋₅₀ WT4 FRA3B chr3:60,235,449–60,601,404 FHIT 28/320 (8.8%) 2326 1.5e−42

FRA5H chr5:58,828,828–59,116,208 PDE4D 10/330 (4.0%) 2326 3.8e−10 FRA7J chr7:69,775,932–69,923,390 AUTS2 8/330 (2.4%) 2326 8.2e−9 FRA16D chr16:78,463,763–78,719,484 WWOX 8/330 (2.4%) 2326 3.6e−7 BS3 FRA1A chr1:10,628,012_{–10,771,196 PEX14, CASZ1} 13/326 (4.0%) 14,667 4.2e₋₉ FRA3B chr3:60,235,449–60,601,404 FHIT 13/326 (4.0%) 14,667 1.3e−4 FRA10G chr10:52,320,213–52,640,424 ASAH2B, A1CF 14/326 (4.3%) 14,667 3.2e−6 FRA16D chr16:78,463,763–78,719,484 WWOX 13/326 (4.0%) 14,667 2.8e−6 BS4 FRA7B chr7:5,880,409–6,120,310 CCZ1, PMS2 11/334 (3.3%) 2128 4.8e−13

FRA7B chr7:6,707,575_{–6,967,206 PMS2CL, CCZ1B} 11/306 (3.6%) 12,724 5.0e₋₅ FRA11G chr11:103,881,556_{–104,600,858 PDGFD} 17/306 (5.6%) 12,724 2.4e₋₅ FRA17q21 chr17:43,517,064–43,840,133 PLEKHM1, LRRC37 10/306 (3.3%) 12,724 6.7e−3

Overview of all SCE hotspots detected in human WT and BS cell lines, as well as frequency of SCE occurrence within hotspots. SCE hotspots were only detected in EBV-transformed cell lines and all hotspots occurred within known CFSs.

quadruplex structures

34

. As before, SCE enrichments were not

affected by SCE subsampling (Supplementary Fig.

4

e). We could

also exclude that enrichments were caused by nucleotide slippage or

high GC content, as SCEs were specifically depleted in genomic

regions with A-rich motifs (A

3+

N

1−7

A

3+

N

1−7

A

3+

N

1−7

A

3+

) or high

GC content across all eight cell lines (Supplementary Fig.

4

f, g).

Taken together, these results support that G4 structures are a major

cause of SCE formation in BS cells.

BS SCEs map to G4 motifs on transcribed strands. As

tran-scription can promote the formation of G4 structures

18

and G4s

were shown to occur mainly in euchromatic regions of the

gen-ome

33

, we hypothesized that BS SCEs occur at G4 motifs in

transcribed genes. We therefore divided all genes into four

categories based on (1) whether genes are active or silent and (2)

the presence or absence of intragenic G4 motifs, and performed a

separate SCE enrichment analysis for each category. We detected

the strongest BS SCE enrichments in transcribed genes containing

at least one G4 motif, whereas non-transcribed genes lacking G4

motifs did not show any significant SCE enrichment patterns

(Fig.

2

c–f). This points to a synergistic effect of transcriptional

activity and the presence of G4 motifs in genes on the enrichment

of SCEs in BS cells.

Intragenic G4 motifs can occur either on the transcribed or

non-transcribed strand (Fig.

3

a) and it is believed that this G4

motif

‘strandedness’ affects how G4 structures influence gene

expression

35

. To assess whether G4 strandedness affects SCE

formation, we separated all intragenic G4 motifs into different

categories based on strandedness and transcriptional status of the

Active genes with G4 motifs

Enrichment

Silent genes with G4 motifs

0.8 0.9 1.0 1.1 1.2 1.3 0.8 0.9 1.0 1.1 1.2 1.3 Enrichment

Silent genes without G4 motifs Cells Cells Cells Enrichment G4 motifs 0.8 0.9 1.0 1.1 1.2 1.3 WT1 WT2 WT3 WT4 BS1 BS2 BS3 BS4 WT1 WT2 WT3 WT4 BS1 BS2 BS3 BS4 WT1 WT2 WT3 WT4 BS1 BS2 BS3 BS4 WT1 WT2 WT3 WT4 BS1 BS2 BS3 BS4 Cells WT1 WT2 WT3 WT4 BS1 BS2 BS3 BS4 Cells WT1 WT2 WT3 WT4 BS1 BS2 BS3 BS4 Cells Enrichment Genes 0.9 1.0 1.1 1.2 Enrichment 0.8 1.0 1.2 0.5 1.0 1.5 2.0 Enrichment

Active genes without G4 motifs

a

b

d

f

0.663 0.334 0.546 0.912 <0.001<0.001 <0.001<0.001 0.042 0.288 0.338 0.191 <0.001 <0.001<0.001 <0.001 0.543 0.993 0.026 0.806 <0.001<0.001 <0.001<0.001 0.617 0.785 0.009 0.005 <0.001 0.014 0.037 0.048 0.889 0.403 0.437 0.773 0.09 0.056 0.004 0.017 0.811 0.931 0.998 0.999 0.074 0.016 0.855 0.003

***

***

***

*

***

***

***

***

***

***

***

***

*

_**

_**

_***

_*

_*

_*

*

**

*

**

*

c

e

Fig. 2 Bloom syndrome SCEs are enriched at G4 motifs in active genes. Relative SCE enrichments (red points) over random distributions (violin plots) for SCEs that overlap with one or multiplea genes; b G4 motifs (G3+N1–7G3+N1-7G3+N1-7G3+);c active genes containing one or more G4 motifs; d active genes

without G4 motifs;e silent genes containing one or more G4 motifs; and f silent genes without G4 motifs. All values were normalized to the median permuted value for overlap of SCEs with FOIs (out of 1,000 permutations) and relative SCE enrichments over these values were plotted on the y-axis. p-values indicate the fraction of permuted overlaps (out of 1,000 permutations) equal to or higher than overlap with observed SCE regions. Significant p-values are indicated as follows: *p < 0.05, **p < 0.01, ***p < 0.001

G4 motifs non-transcribed strand silent genes 0.7 0.8 0.9 1.0 1.1 1.2 Enrichment Enrichment

G4 motifs transcribed strand silent genes

0.7 0.8 0.9 1.0 1.1 1.2 Enrichment

G4 motifs non-transcribed strand active genes

0.8 1.0 1.2

Enrichment

G4 motifs transcribed strand active genes

0.8 1.0 1.2

Enrichment

Enrichment

G4 motifs non-transcribed strands

0.8 0.9 1.0 1.1 1.2 Cells Enrichment

G4 motifs transcribed strands

0.9 1.0 1.1 1.2 WT1 WT2 WT3 WT4 BS1 BS2 BS3 BS4 Cells WT1 WT2 WT3 WT4 BS1 BS2 BS3 BS4 Cells WT1 WT2 WT3 WT4 BS1 BS2 BS3 BS4 Cells WT1 WT2 WT3 WT4 BS1 BS2 BS3 BS4 Cells WT1 WT2 WT3 WT4 BS1 BS2 BS3 BS4 Cells WT1 WT2 WT3 WT4 BS1 BS2 BS3 BS4 Cells WT1 WT2 WT3 WT4 BS1 BS2 BS3 BS4 0.564 0.958 0.697 0.669 0.019 0.056 0.186 0.011 0.563 0.947 0.924 0.693 <0.001 0.148 0.705 0.647 0.887 0.809 0.034 0.258 <0.001<0.001<0.001<0.001 0.985 0.717 0.023 0.258 <0.001 0.150 0.003 0.263 0.707 0.994 0.992 0.970 0.981 0.636 0.917 0.245 0.395 0.996 0.999 0.920 0.991 0.830 0.997 0.663 G4 on transcribed strand G4 on non-transcribed strand Enrichment Intergenic G4 motifs 0.9 1.0 1.1 1.2 0.386 0.722 0.958 0.978 0.597 0.074 0.101 <0.001

***

*

*

***

***

***

***

***

***

**

a

b

d

c

f

e

h

g

Fig. 3 Bloom syndrome SCE enrichments occur at transcribed G4 motifs. a Intragenic G4 motifs can occur on either the transcribed strand (RNA shown in red), or on the non-transcribed strand;b–h relative SCE enrichments (red points) over random distributions (violin plots) for SCEs overlapping with h for intergenic G4 motifs, and G4 motifs occurring onc intragenic transcribed strands; d transcribed strands; e transcribed strands of active genes; f non-transcribed strands for active genes;g transcribed strands for silent genes; and h non-transcribed strands for silent genes.p-values indicate the fraction of permuted overlaps (out of 1,000 permutations) equal to or higher than overlap with observed SCE regions. Significant p-values are indicated as follows: *p < 0.05, **p < 0.01, ***p < 0.001

gene, and performed SCE enrichment analysis for these locations.

Although we found no evidence of SCE enrichments at intergenic

G4 motifs (Fig.

3

b), SCE are enriched at intragenic G4 motifs on

both transcribed and non-transcribed strands (Fig.

3

c, d).

Furthermore, BS-specific SCE enrichments were higher on

transcribed than on non-transcribed strands, this effect is even

strongest for G4 motifs on active transcribed genes (Fig.

3

e, f).

Strikingly, no SCE enrichments were detected for either

0.8 1.2 1.6

Enrichment

G4 motifs non-transcribed strand silent genes 0.8 1.2 1.6

Enrichment

G4 motifs transcribed strand silent genes 0.6 0.8 1.0 1.2 1.4 1.6 Enrichment

G4 motifs transcribed strand active genes

0.6 0.8 1.0 1.2 1.4 1.6 Enrichment

G4 motifs non-transcribed strand active genes

Enrichment 0.6 0.8 1.0 1.2 1.4 1.6 WT

C1.Blm_KO C2.Blm_KO C3.Blm_KO C4.Blm_Het

Cells

WT

C1.Blm_KO C2.Blm_KO C3.Blm_KO C4.Blm_Het

Cells

WT

C1.Blm_KO C2.Blm_KO C3.Blm_KO C4.Blm_Het

Cells

WT

C1.Blm_KO C2.Blm_KO C3.Blm_KO C4.Blm_Het

Cells

WT

C1.Blm_KO C2.Blm_KO C3.Blm_KO C4.Blm_Het

Cells

WT

C1.Blm_KO C2.Blm_KO C3.Blm_KO C4.Blm_Het

Cells

WT

C1.Blm_KO C2.Blm_KO C3.Blm_KO C4.Blm_Het

Cells Genes 0.9 1.0 1.1 Enrichment G4N7 motifs 0.054 <0.001 <0.001 0.002 0.001 0.728 <0.001 0.001 0.003 0.023 0.861 0.001 <0.001 <0.001 0.068 0.889 0.004 <0.001 0.083 0.009 0.734 0.056 0.113 0.050 0.068 0.332 0.038 0.046 0.011 0.412 *** ** *** *** ** ** * *** *** *** *** *** ** ** * * * SCEs/library 80 60 40 20 0 p = 1.4e–27 p = 6.7e–53 p = 2.9e–12 % Matching alleles SCE/G4 100 75 50 25 0 WT

C1.Blm_KOC2.Blm_KOC3.Blm_KOC4.Blm_Het

Discordant cast/EiJ NS p = 0.106 (n=41) p = 0.001 (n=34) p = 0.002 (n=71) p = 0.005 (n=52) NS p = 0.623 (n=10) Discordant 129Sv Concordant cast/EiJ Concordant 129Sv

**

**

**

a

b

c

e

d

f

g

_h

transcribed or non-transcribed strand G4 motif in silent genes

(Fig.

3

g, h). These results confirm the synergistic effect of

transcriptional activity and the presence of a G4 motif as a trigger

for SCE formation in BS cells.

SCEs map to G4 motifs in both human and murine

BLM

−/−

cells. To confirm that the BS SCE enrichment patterns we

detected in human cells are a direct result of BLM deficiency, we

next generated Blm knockout cells in an F1 hybrid mouse

embryonic stem (ES) cell line (129Sv-Cast/EiJ) by means of the

Crispr/Cas9 technology. We used different combinations of two

guide RNAs to generate loss-of-function mutants by deleting Blm

exon 19, which is critical for Blm’s role in both Holliday junction

resolution

36

and G4 unwinding

37

. We selected three homozygous

and one heterozygous clones with the desired deletions and

characterized these deletions by Sanger sequencing

(Supplemen-tary Table

2

), measured Blm mRNA expression levels by

quan-titative reverse-transcriptase PCR (qRT-PCR) (Supplementary

Fig.

5

a), and confirmed the elevated SCE rates by Strand-seq

(Fig.

4

a and Supplementary Fig.

5

b, c). Interestingly, we detected

intermediately high SCE rates in the Blm

+/−

cells, even though

previous studies reported that cells from heterozygous family

members of BS patients display normal SCE levels

2,38

. Similar to

that for the human cells, SCEs in libraries made from the ES cells

could be mapped at kilobase resolution (Supplementary Table

2

).

Using the identified SCE regions, we performed the same

analysis as described above for the human cell lines. As before, we

generated RNA-seq data for each of ES cell clones to assess the

effect of transcriptional activity and G4 strandedness on SCE

enrichments. Although we did not detect any clear increased SCE

enrichments in genes for the Blm mutant cell lines (Fig.

4

b) or an

effect of transcriptional activity (Supplementary Fig 5d, e), we did

confirm that these cells display SCE enrichments at canonical and

alternative G4 motifs (Fig.

4

c and Supplementary Fig.

6

a, b). We

detected significant SCE enrichments at sites of intragenic G4

motifs occurring on both transcribed and non-transcribed strands

in the absence of Blm (Supplementary Fig.

6

c, d) and confirmed

that SCE enrichments in absence of Blm are strongest at G4 motifs

occurring on transcribed strands in active genes (Fig.

4

d–g). As in

the human cell lines, we found no SCE enrichments at sites of

intergenic G4 motifs (Supplementary Fig.

6

e).

The F1 hybrid ES cells we used to generate our Blm mutants

contain over 20 million known heterozygous positions, including

72,660 canonical G4 motifs that only occur on one homolog

(36,547 in the 129 Sv background, and 36,203 in the Cast/EiJ

background). To

find further evidence of a direct link between

G4s and SCEs, we identified all observed SCE regions that overlap

a single discordant G4 motif, and the homologs that these SCEs

occurred on. We found that on average, 69% of informative SCEs

in the Blm

−/−

cell lines occurred on the same homolog as the G4

Fig. 4 Confirmation of SCE enrichments at G4 motifs in Blm−/−mouse ES cells.a SCE rates detected WT,Blm−/−, andBlm+/−mouse ES cells. Each grey point represents number of SCEs detected in a single-cell Strand-seq library, red lines indicate mean± SD. p-values were calculated using t-test and ANOVA.b_{–g Relative SCE enrichments (red points) over random distributions (violin plots) for SCEs overlapping one or more (b) genes; c G4 motifs; and} G4 motifs occurring ond transcribed strands of active genes; e transcribed strands for active genes; f transcribed strands for silent genes; and g non-transcribed strands for silent genes.p-values indicate the fraction of permuted overlaps (out of 1,000 permutations) equal to or higher than overlap with observed SCE regions. Significant p-values are indicated as follows: *p < 0.05, **p < 0.01, ***p < 0.001. h Frequency of observed SCE regions occurred on the same homolog as allele-specific G4 motifs. Indicated is the homolog containing G4 motif, concordant indicates SCE occurred on same homolog, discordant indicates SCE occurred on opposite homolog. Number of allelic G4 motifs included in analysis is shown above each bar.p-values were calculated using binomial distributions based on a 50% chance of SCE and G4 motif occurring on the same homolog

100 90 80 70 60 50 40 30 20 10 0

% Libraries with LOH region

Chromosome count

Number of CNVs

Cell line + Passage nr

Cell line + Passage nr

Loss of heterozygosity Aneuploidy

Local copy number variations WT

n=77 n=72 n=77 n=79 n=75 n=83 n=80 n=60 n=79 n=83 n=50 n=76 C1.Blm_KO C2.Blm_KO C3.Blm_KO

p = 0.34 p = 0.073 p = 0.001 p = 0.004 p = 0.13 p = 0.38 P0 P20 P30 P0 P20 P30 P0 P20 P30 P0 P20 P30 P0 P20 P30

Cell line + Passage nr

P0 P20 P30 WT C1.Blm_KO C2.Blm_KO C3.Blm_KO 13qA2 1qB 15qD1 8qC2 None 55 50 45 40 8 6 4 2 0 p < 0.001 p < 0.001 p < 0.001 WT C1.Blm_KO C2.Blm_KO C3.Blm_KO

a

b

c

Fig. 5 Low levels of loss of heterozygosity inBlm−/−mouse ES cells. Frequency ofa unique LOH regions; b aneuploidy; and c local copy number variations detected at in single-cell whole genome sequencing libraries at different passages in WT,Blm−/−, andBlm+/−mouse ES cells. The number of single-cell sequencing libraries included in the analysis is shown above each bar.p-values for LOH events were calculated using binomial distributions, for aneuploidy and CNVs by ANOVA

motifs, which is significantly different (p < 0.01) from the

expected 50% if there was no causal relationship between G4

motifs and SCEs (Fig.

4

h). No significant deviation from the

expected 50/50 ratio was detected in the WT or the Blm

+/−

cell

lines. Combined, these results confirm that SCEs mainly form at

G4 structures in absence of Blm and especially at those G4s

present in the transcribed strands of active genes.

LOH is not signi

ficantly increased in Blm

−/−

cells. As SCEs are

exchanges of genetic material between identical sister chromatids,

they normally do not result in any mutations. However, if an

exchange event occurs between homologs instead of sister

chro-matids, this can lead to LOH

39

. It has previously been shown that

BLM deficient cells display elevated levels of LOH

12–14

_{. However,}

these results were obtained using systems that rely on selection of

cells that underwent LOH at a specific locus. Using our F1 ES cell

lines, we could detect and track LOH events throughout the entire

genome based on single-nucleotide polymorphisms between the

parental mouse strains. To do this, we kept the WT and Blm

mutant ES cells in continuous culture for 30 passages (~ 75 cell

divisions), which would result in 3.8 × 10

22

offspring cells for each

parental cell, compared to an estimated 1.2 × 10

10

cells in an adult

mouse body. We performed single-cell whole-genome sequencing

(scWGS) at different timepoints (passages 0, 20, and 30), and

identified chromosomal regions that underwent LOH (see

Methods). We also identified chromosomal and local copy

number variations (CNVs) to confirm that LOH regions are not

caused by deletions, and to determine if the Blm

−/−

cells display

aberrant levels of CNVs.

We did not detect a single LOH region in the WT cells at any

of the three time points, and only four unique LOH regions in the

three Blm

−/−

clones (Fig.

5

a). Two of these four regions were

detected in a single library at a single time point, while the two

others were detected at multiple time points and their frequency

increased over time. However, these more frequent LOH regions

occurred on chromosomes 1 and 8, both of which display

increasing levels of trisomy in all four cell lines (Supplementary

Fig.

7

). This suggests that trisomy led to clonal expansion within

the cell populations, and the detected LOH regions had no effect

on cellular proliferation. Although these results do point towards

elevated LOH in Blm

−/−

cells, the differences are not significant

and suggest that LOH is an uncommon occurrence, even in the

absence of Blm.

BLM has been linked to chromosome segregation

40

and

BLM-deficient cells display a higher frequency of micronuclei

41

_{, both of}

which can result in aneuploidy. When we assessed the WT and

Blm

−/−

cells for instances of local and chromosomal CNVs, we

No BLM > dHJ resolution MUS81/EME1 or GEN1 No BLM > no G4 unwinding

replication fork stalling

Recombination: bypass G4 lesion by polymerase template switch

Return polymerase to original template strand: dHJ formation G4 formation

BLM deficiency Active transcription

G4 motif on transcribed strand

Active BLM > dHJ dissolution BTRR complex

No sister chromatid exchange Sister chromatid exchange

Fig. 6 BLM helicase suppresses recombination at G4 structures. Model for the role of BLM in suppressing recombination at sites of G4 structures. G4 structures are more likely to form or persist in the absence of unwinding by BLM. They can form at G4 motifs throughout the genome, but formation is promoted by transcription, especially if the G4 motif is present on the transcribed strand. In BLM proficient cells, BLM unwinds the G4 structure before the genomic region is replicated, ensuring smooth DNA replication. In the absence of BLM, G4 structures are not unwound, preventing replication fork progression, and leading to replication fork stalling. Stalled forks require homologous recombination (HR)-mediated repair, leading to formation of a double Holliday junction (dHJ). In the absence of BLM, dHJs cannot be dissolved by the BLM-TOPO3α-RMI1-RMI2 (BTRR) complex and must be resolved by MUS81-EME1 or GEN1, leading to frequent formation of sister chromatid exchanges event, and potentially loss of heterozygosity, and other types of mutations

found that although there are significant differences between the

individual cell lines, no trend can be seen indicating that Blm

−/−

cells contain more or fewer such events (Fig.

5

b, c).

Discussion

Elevated SCE rates are a hallmark feature in cells from BS

patients

2,3

, but the exact mechanism behind this phenotype is not

fully understood. A major obstacle to unravelling the cause of BS

SCEs was that SCEs cannot be accurately mapped using standard

cytogenetic detection methods. For this study, we used Strand-seq

for SCE detection, as this technique does allow for

high-resolution mapping. Even though the technique is limited by

loss of DNA during preparation of single-cell sequencing

librar-ies, leading to low coverage within individual libraries (~ 1–2%

genome coverage), we show here that SCEs in both normal and

BS cells could be mapped at kilobase resolutions, allowing for

robust analysis on SCE locations and thus their causes.

We show that SCEs frequently occur at sites of G4 structures in

both BLM deficient human and murine cells. While there does

not appear to be a direct effect of transcriptional activity on SCE

enrichment patterns, strong SCE enrichments in BS cells were

observed in transcribed genes containing one or more G4 motifs,

especially when the G4 motif was present on the transcribed

DNA strand. The observation that SCEs were enriched on

homolog-specific G4 motifs in the Blm mutant ES cells provides

further evidence that, in the absence of BLM, G4 structures can

directly trigger SCE formation.

Studies of G4 structures have been hampered by their high

stability, making them resistant against several nucleases

42

and

difficult to analyze using standard PCR conditions

34

_{. The use of}

Strand-seq bypasses these issues, because SCE regions are

iden-tified as the region between sequencing reads. As such, any SCE

overlap with G4 motifs requires that the G4 motif lies within the

identified SCE region and therefore does not have to be covered

by sequencing reads itself.

Previous studies have shown that BLM is required for

unwinding G4 structures during telomere replication

43

, and that

it has a role in regulating expression of genes containing G4

motifs

44,45

. Our study is the

first to directly implicate

G4 structures in the increased recombination and genome

instability in cells that lack BLM. These results are consistent with

proposed models of BLM unwinding G4 structures during DNA

replication

37,46

and with previous reports that BLM binds and

unwinds G4 structures in vitro

15,16

. Our results show that BLM is

required to unwind G4 structures throughout the genome.

G4 structures are known to pose barriers for DNA replication

20

and previous studies have shown that specialized helicases such as

Dog-1, Pif1 and FANCJ are required to prevent instability at

G-rich genomic DNA in Caenorhabditis elegans

47

, yeast

48

, and

man

49

. The fact that such other helicases cannot compensate for

loss of BLM suggests that these helicases do not have redundant

functions, but are either specific for subsets of G4 structures, or

that they cooperate to unwind G4 structures, as was proposed for

BLM and FANCJ

50,51

. Consistent with this, BLM deficient cells

display elevated levels of G4 structures at telomeres

43

, and it

seems logical that this holds true throughout the genome. We

propose that failure to unwind G4 structures in BLM-deficient

cells leads to stalled replication forks, which trigger

recombina-tion and genome instability (Fig.

6

).

SCEs in cells lacking BLM were found to frequently occur in

transcribed genes, supporting that such sites are subject to higher

mutation rates

27

. Elevated intragenic mutations rates are likely to

contribute to the strong cancer predisposition associated with BS.

This also helps explain a unique feature of BS, which predisposes

patients to a wide range of cancers instead of towards specific

types of tumors

1

. Combined with elevated LOH levels, the

pro-posed chromosome fragility is likely to play a role in the strong

cancer predisposition associated with the syndrome.

Methods

Cell cultures. The following cell lines were obtained from the Corriell Cell Repository: GM07492 and GM07545 (primaryfibroblasts, normal), GM02085 and GM03402 (primaryfibroblasts, BS), GM12891 and GM12892 (EBV-transformed lymphocytes, normal), and GM16375 and GM17361 (EBV-transformed B-lymphocytes, BS). The WT hybrid mouse ES cell line F121.6 (129Sv-Cast/EiJ) was a kind gift from Joost Gribnau (Erasmus University, Rotterdam, The Netherlands).

Fibroblasts were cultured in Dulbecco's modified Eagle's medium (DMEM) (Life Technologies) supplemented with 10% v/v fetal bovine serum (FBS) (Sigma Aldrich) and 1% v/v penicillin–streptomycin (Life Technologies), B-lymphocytes in RPMI1640 (Life Technologies) supplemented with 15% v/v FBS and 1% v/v penicillin–streptomycin.

ES cells were cultured on mitotically arrested mouse embryonicfibroblast cells in DMEM (Life Technologies), supplemented with 15% v/v FBS (Bodinco BV), 1% v/v penicillin–streptomycin, 1% v/v non-essential amino acids (Life Technologies), 50µM 2-mercaptoethanol (ThermoFisher Scientific), and 1,000 U ml−1_leukemia

inhibitor factor (Merck). All cells were cultured at 37 °C in 5% CO2. For

Strand-seq, BrdU (Invitrogen) was added to exponentially growing cell cultures at 40µM final concentration. Timing of BrdU pulse was 12 h for ES cells, 18 h for fibroblast cell lines, and 24 h for B-lymphocyte cell lines.

Generation of Blm mutant ES cell lines. Blm mutants were generated using CRISPR/Cas9 genome editing. sgRNAs were designed to cleave the Blm gene at sitesflanking exon 19 and cloned into PX459 plasmid52. Combinations of two plasmids (30μg each) were transfected into F121.6 cells by means of electro-poration (Biorad Genepulser XL). Cells were incubated for 24 h before puromycin (1µg/ml) was added to cell culture medium. After 48 h of selection, resistant colonies were left to grow, picked and expanded. Screening for Blm mutant clones was performed by allele-specific PCR of genomic region containing putative deletion.

qRT-PCR analysis. Exponentially growing cells were collected and RNA was isolated using the Nucleospin RNA kit (Macherey Nagel). Reverse transcription was performed using Superscript II Reverse Transcriptase (Invitrogen) with ran-dom hexamers (Invitrogen). Quantitative PCR was performed using SYBR Green I Master (Roche) on the LightCycler480 (Roche).

Strand-seq and scWGS library preparation. For Strand-seq and WGS, expo-nentially growing cells were collected after BrdU pulse (for Strand-seq) or without any treatment (WGS), and resuspended in nucleo isolation buffer (100 mM Tris-HCl pH 7.4, 150 mM NaCl, 1 mM CaCl2, 0.5 mM MgCl2, 0.1% NP-40, and 2%

bovine serum albumin) supplemented with 10µg ml−1Hoechst 33,258 (Life Technologies) and propidium iodide (Sigma Aldrich). Single nuclei were sorted into 5µl Pro-Freeze-CDM NAO freeze medium (Lonza) + 7.5% dimethyl sulfoxide, in 96-well skirted PCR plates (4Titude), based on low propidium iodide and low Hoechstfluorescence using a MoFlo Astrios cell sorter (Beckman Coulter) or a FACSJazz cell sorter (BD Biosciences). DNA from single cells was processed for Strand-seq23_{or WGS}53_{. For each experiment, 96 libraries were pooled and}

250–450 bp-sized fragments were isolated and purified. DNA quality and con-centrations were assessed using the High Sensitivity dsDNA kit (Agilent) on the Agilent 2100 Bio-Analyzer and on the Qubit 2.0 Fluorometer (Life Technologies). RNA-seq library preparation. Exponentially growing cells were harvested and RNA was isolated using the Nucleospin RNA kit (Macherey Nagel). RNA-sequencing libraries were prepared using the NEBNext Ultra RNA Library Prep kit for Illumina (NEB) combined with the NEBNext rRNA Depletion kit (NEB). Complementary DNA quality and concentrations were assessed using the High Sensitivity dsDNA kit (Agilent) on the Agilent 2100 Bio-Analyzer and on the Qubit 2.0 Fluorometer (Life Technologies).

Illumina sequencing. Clusters were generated on the cBot (HiSeq2500) and single-end 50 bp reads (Strand-seq and RNA-seq) or paired-single-end 150 bp reads (scWGS) were generated were generated using the HiSeq2500 sequencing platform (Illumina).

Bioinformatics

Genome alignment. Indexed bamfiles were aligned to human (GRCh37) or mouse genomes (GRCm38) using Bowtie254_{for Strand-seq and scWGS libraries, and}

STAR aligner55_{for RNA-seq libraries.}

Sister chromatid exchange detection. SCE were identified and mapped with the BAIT software package56_{, using standard settings. As BAIT also detects stable}

> 5% of cells from one cell line were excluded from the analysis. SCEs were assigned to homologs by splitting.bamfiles into separate files for each genetic background based on reads covering informative polymorphisms and using BAIT to identify on which homologs SCEs occurred.

Detection and analysis of SCE hotspots. BAIT-generated.bed files containing the locations of all mapped SCEs were uploaded to the USCS genome browser and hotspots were identified as regions containing multiple overlapping SCEs. p-values were assigned to putative SCE hotspots using a custom R-script based on capture–recapture statistics. Briefly, the genome was divided into bins of the same size as the putative hotspot and the chance offindings the observed number of SCEs in one bin was calculated based on the total number of SCEs detected in the cell line.

Enrichment analysis. A custom Perl script was used for the permutation model. For each of 1,000 permutations, we generated a random number n and shifted all SCEs downstream by n bases on the same chromosome. To prevent small-scale local shifts, we required n to be a random number between 2 and 50 Mbp. If the resulted coordinate exceeded chromosome size we subtracted the size of chromosome, so that the SCE is mapped to beginning part of the chromosome, as if the chromo-some was circular. We also excluded all annotated assembly gaps before our analysis, to prevent permuted SCE mapping to one of the gap regions. We then determined the number of SCEs overlapping with a feature of interest in each permutation, as well as the original SCE regions. All values were normalized to the median permutated value, in order to determine relative SCE enrichments over expected, randomized distributions and to allow for comparison of the different cell lines. Significance was determined based on how many permutations showed the same or exceeding (enrichment) or the same or receding (depletion) overlap with a given genomic feature compared to overlap between the original SCEs and the same feature. Any experimental overlap that lies outside of the 95% confidence interval found in the permutations has a p-value below 0.05 and was deemed significant. Experimental overlaps lying outside of the permuted range were given a p-value below 0.001, as there was a<0.1% (1/1,000) chance of such an overlap occurring by chance. Enrichment analyses for G4 motifs were performed using a 10 Kb SCE region size cutoff, enrichment analysis for genes and promoter regions used a 100 Kb size cutoff, unless specified otherwise. Genome and gene annotations were obtained from Ensembl release 75 (GRCh37 assembly,http://www.ensembl. org). Gene bodies were defined as regions between transcription start sites and

transcription end sites, gene promoters as 1 Kbp regions upstream of transcription start sites. Putative G4 motifs were predicted using custom Perl script by matching genome sequence against following patterns: G3+NxG3+NxG3+NxG3+, where x could

be the ranges of 1–3, 1–7, or 1–12 bp.

RNA-seq analysis. Mapped reads were aligned and quantified using STAR aligner55_.

FPKM values were calculated for all genes and based on these genes were assigned active (FPKM> 1) or silent (FPKM < 1) status.

Aneuploidy and CNV detection. Aligned libraries were analyzed as previously described using AneuFinder R package57using the following settings: low-quality alignments (mapping quality score (MAPQ)< 10) and duplicate reads were excluded and read counts in 2 Mb variable-width bins were determined with a 10-state Hidden Markov Model with copy-number 10-states: zero-inflation, null-, mono-, di-, tri-, tetra-, penta-, hexa-, septa-, and octasomy.

LOH detection. Reads were aligned to either 129 Sv or Cast/EiJ genetic background based on covered single-nucleotide polymorphisms (SNPs). Reads lacking infor-mative SNPs were discarded. Reads (129 Sv) were assigned a positive (Crick) orientation, Cast/EiJ reads a negative (Watson) orientation. The resulting.bamfiles were analyzed using BAIT and LOH events were detected as switches from mixed background to pure 129 Sv or Cast/EiJ background in the absence of deletions (as detected using AneuFinder).

Data availability. The Strand-seq, scWGS, and RNA-seq data reported in this paper have been submitted to the Arrayexpress database under accession E-MTAB-5976. SCE enrichment analysis software is available through GitHub (https:// github.com/Vityay/GenomePermute).

Received: 5 October 2017 Accepted: 21 December 2017

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Acknowledgements

We thank Dirk Hockemeyer, Marcel van Vugt, and Peter Stirling for critical reading of this manuscript, Inge Kazemier and Karina Hoekstra-Wakker for technical assistance, and Ester Falconer, Mark Hills, and all members of the Lansdorp laboratories in Van-couver and Groningen for discussions and feedback. Financial support was provided by an Advanced Grant from the European Research Council to P.M.L.

Author contributions

N.v.W. and P.M.L. conceived and designed the study. N.v.W. and S.M. created and characterized Blm mutant cell lines. N.v.W. and N.H. performed Strand-seq, scWGS, and RNA-seq experiments. D.C.S.J. supervised next-generation sequencing efforts. N.v.W. and V.G. analysed sequencing data. N.v.W. wrote the manuscript with assistance from S.M., P.M.L. and all authors. P.M.L. supervised the project.

Additional information

Supplementary Informationaccompanies this paper at https://doi.org/10.1038/s41467-017-02760-1.

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